Object Oriented Programming with Python

Introduction

Data science requires the design of computer-intensive algorithms and routines, which are relatively easy to manage and further develop. In addition, many business relevant cases require handling large amounts of data in an efficient manner. This course gives you the knowledge and skills needed to program such algorithms using object-oriented programming. Further, to improve your productivity as a business-oriented data scientist you will learn how to keep track of your code as it evolves and facilitate peer collaboration.

Course content

• Recap: 
  - Datatypes, expressions, boolean variables, functions, loops and conditional statements

• Introduction to object-oriented programming:
  - Classes, constructors and methods

• Further topics in objected-oriented programming:
  - Inheritance, superclasses, and subclasses

• Version control with Git and Github

          - Pull, commit, and push. 

          - Branching and merching

• Parser for command line and parallelize functions.
• Debugging and finding errors.
• Technologies to handle large amounts of data.